Acceptable performance of blood biomarker tests of amyloid pathology - recommendations from the Global CEO Initiative on Alzheimer's Disease.

Anti-amyloid treatments for early symptomatic Alzheimer disease have recently become clinically available in some countries, which has greatly increased the need for biomarker confirmation of amyloid pathology. Blood biomarker (BBM) tests for amyloid pathology are more acceptable, accessible and scalable than amyloid PET or cerebrospinal fluid (CSF) tests, but have highly variable levels of performance. The Global CEO Initiative on Alzheimer's Disease convened a BBM Workgroup to consider the minimum acceptable performance of BBM tests for clinical use. Amyloid PET status was identified as the reference standard. For use as a triaging test before subsequent confirmatory tests such as amyloid PET or CSF tests, the BBM Workgroup recommends that a BBM test has a sensitivity of ≥90% with a specificity of ≥85% in primary care and ≥75-85% in secondary care depending on the availability of follow-up testing. For use as a confirmatory test without follow-up tests, a BBM test should have performance equivalent to that of CSF tests - a sensitivity and specificity of ~90%. Importantly, the predictive values of all biomarker tests vary according to the pre-test probability of amyloid pathology and must be interpreted in the complete clinical context. Use of BBM tests that meet these performance standards could enable more people to receive an accurate and timely Alzheimer disease diagnosis and potentially benefit from new treatments.

A framework for addressing Alzheimer's disease: Without a frame, the work has no aim.

Confronting Alzheimer's disease (AD) involves patients, healthcare professionals, supportive services, caregivers, and government agencies interacting along a continuum from initial awareness to diagnosis, treatment, support, and care. This complex scope presents a challenge for health system transformation supporting individuals at risk for, or diagnosed with, AD. The AD systems preparedness framework was developed to help health systems identify specific opportunities to implement and evaluate focused improvement programs. The framework is purposely flexible to permit local adaptation across different health systems and countries. Health systems can develop solutions tailored to system-specific priorities considered within the context of the overall framework. Example metric concepts and initiatives are provided for each of ten areas of focus. Examples of funded projects focusing on screening and early detection are provided. It is our hope that stakeholders utilize the common framework to generate and share additional implementation evidence to benefit individuals with AD.

Toward comprehensive value assessment for Alzheimer's disease innovations.

INTRODUCTION: Assessing medical technologies for Alzheimer's disease (AD) creates challenges for current methods of value assessment. New value assessment approaches for AD are also needed. METHODS: We adapted concepts from health economics to help guide decision makers to more informed decisions about AD therapies and diagnostics. RESULTS: We propose a value framework based on five categories: perspective, value elements, analysis, reporting, and decision making. AD value assessments should include the perspective of the patient-caregiver dyad. We propose a broader array of value elements than currently used. Analytics and decision methods can synthesize evidence for all elements of value. Decisions should use a "deliberative appraisal" approach informed by the composite evidence and be transparently reported. DISCUSSION: Using the proposed framework, the value of forthcoming innovations for AD may be more thoroughly assessed for and by all stakeholders. It can guide decision makers to carefully consider all relevant elements of value contributing to more holistic and transparent decision making. RESEARCH HIGHLIGHTS: Alzheimer's disease challenges common methods of evaluating medical technology. Using current methods, new AD innovations might not be appropriately valued. Poor value assessments will adversely affect patient access to AD innovations. A full AD value framework expands perspective, elements, analysis, decision-making, reporting.

A review of stakeholder recommendations for defining fit-for-purpose real-world evidence algorithms.

Aim: The credibility and value of real-world evidence (RWE) are either supported or undermined by the algorithms (i.e., operational definitions) used. Methods: We conducted a targeted evidence review of key RWE decision makers' published recommendations on RWE algorithms through April 2021. Stakeholders were regulatory bodies, other governmental agencies and payer organizations. Results: Our review identified recommended criteria: relevance, validity, reliability, responsiveness, transparency and replicability, safety, feasibility and quality process. Stakeholders routinely recommended accuracy measures, subgroups evaluation and specific considerations for assessing exposures and covariates and the underlying real-world data (RWD) quality. Conclusion: The importance of stakeholder guidance on fit-for-purpose RWE algorithms is growing. We highlight gaps that future guidance and stakeholder recommendations could address.

Replication of randomized clinical trial results using real-world data: paving the way for effectiveness decisions.

The FDA is preparing guidance about using real-world evidence (RWE) to support decisions about product effectiveness. Several ongoing efforts aim to replicate randomized clinical trial (RCT) results using RWE with the intent of identifying circumstances and methods that provide valid evidence of drug effects. Lack of agreement may not be due to faulty methods but rather to the challenges with emulating RCTs, differences in healthcare settings and patient populations, differences in effect measures and data analysis, bias, and/or the efficacy-effectiveness gap. In fact, for some decisions, RWE may lead to better understanding of how treatments work in usual care settings than a more constrained view from RCTs. Efforts to reconcile the role and opportunities for generating complementary evidence from RWE and RCTs will advance regulatory science.

Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force.

The allocation of healthcare resources among competing priorities requires an assessment of the expected costs and health effects of investing resources in the activities and of the opportunity cost of the expenditure. To date, much effort has been devoted to assessing the expected costs and health effects, but there remains an important need to also reflect the consequences of uncertainty in resource allocation decisions and the value of further research to reduce uncertainty. Decision making with uncertainty may turn out to be suboptimal, resulting in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to better inform resource decisions. This value can be quantified using value of information (VOI) analysis. This report from the ISPOR VOI Task Force describes methods for computing 4 VOI measures: the expected value of perfect information, expected value of partial perfect information (EVPPI), expected value of sample information (EVSI), and expected net benefit of sampling (ENBS). Several methods exist for computing EVPPI and EVSI, and this report provides guidance on selecting the most appropriate method based on the features of the decision problem. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing VOI analyses. The software needed to compute VOI is discussed, and areas for future research are highlighted.

Value of Information Analysis for Research Decisions-An Introduction: Report 1 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force.

Healthcare resource allocation decisions made under conditions of uncertainty may turn out to be suboptimal. In a resource constrained system in which there is a fixed budget, these suboptimal decisions will result in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to make better resource allocation decisions. This value can be quantified using a value of information (VOI) analysis. This report, from the ISPOR VOI Task Force, introduces VOI analysis, defines key concepts and terminology, and outlines the role of VOI for supporting decision making, including the steps involved in undertaking and interpreting VOI analyses. The report is specifically aimed at those tasked with making decisions about the adoption of healthcare or the funding of healthcare research. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing the results of VOI analyses.

Getting real performance out of pay-for-performance.

CONTEXT: Most private and public health insurers are implementing pay-for-performance (P4P) programs in an effort to improve the quality of medical care. This article offers a paradigm for evaluating how P4P programs should be structured and how effective they are likely to be. METHODS: This article assesses the current comprehensiveness of evidence-based medicine by estimating the percentage of outpatient medical spending for eighteen medical processes recommended by the Institute of Medicine. FINDINGS: Three conditions must be in place for outcomes-based P4P programs to improve the quality of care: (1) health insurers must not fully understand what medical processes improve health (i.e., the health production function); (2) providers must know more about the health production function than insurers do; and (3) health insurers must be able to measure a patient's risk-adjusted health. Only two of these conditions currently exist. Payers appear to have incomplete knowledge of the health production function, and providers appear to know more about the health production function than payers do, but accurate methods of adjusting the risk of a patient's health status are still being developed. CONCLUSIONS: This article concludes that in three general situations, P4P will have a different impact on quality and costs and so should be structured differently. When information about patients' health and the health production function is incomplete, as is currently the case, P4P payments should be kept small, should be based on outcomes rather than processes, and should target physicians' practices and health systems. As information improves, P4P incentive payments could be increased, and P4P may become more powerful. Ironically, once information becomes complete, P4P can be replaced entirely by "optimal fee-for-service."

The impact of consumer-directed health plans on prescription drug use.

There has been much debate over the merits of consumer-directed health plans (CDHPs), yet there is little empirical evidence of their influence on health care use. We examined patterns in prescription drug use in the first year that CDHPs were offered alongside traditional plans. Using pharmacy claims data from one large company, we found that enrollees in high-deductible CDHPs were much more likely than those with other coverage to discontinue two of five drug classes. Enrollment in a CDHP did not, however, reduce adherence among those continuing their medication, nor did it greatly influence the use of generic drugs.

Impact of computer-generated personalized goals on cholesterol lowering.

OBJECTIVES: The National Cholesterol Education Program (NCEP) has enhanced public awareness of the importance of cholesterol in the development of heart disease, yet most patients with cardiovascular disease (CVD) do not know or achieve their low-density lipoprotein cholesterol (LDL-C) goals. This randomized, controlled trial was designed to evaluate the impact of a system that provides uniquely formatted laboratory results to patients with CVD on their changes in LDL-C levels. METHODS: Eighty patients with CVD were randomized to receive standard care or the intervention inclusive of a computer-generated, 11''x17'' color poster depicting an individual's LDL-C status and goals along with personalized steps to aid in goal achievement. Cholesterol profiles were obtained at baseline and 6 months after enrollment. Physicians received standard laboratory reports and were blinded to the randomization. RESULTS: There were no significant differences between patient groups in age, education level, race, baseline cholesterol levels, comorbidities, or percentage of patients in each group who met their NCEP goal at baseline. Patients receiving intervention tools had significant reductions in LDL-C from baseline compared with patients in the control group. Intervention patients who did not meet NCEP goals at baseline had the greatest reduction in LDL-C, with a mean change from baseline of -21.5 mg/dL (P<0.001) whereas standard care patients had no significant change in the LDL-C levels (-4.6 mg/dL, P=0.28). At study close, 73% of intervention patients reported that their posters remained displayed on their refrigerator. CONCLUSION: This unique and personalized intervention resulted in the LDL-C lowering benefit among patients with CVD comparable to that of lipid lowering agents.

Aligning quality for populations and patients: do we know which way to go?

Both the medical care and public health systems have invested considerable resources to define, measure, and improve quality and health outcomes. A movement toward accountability has generated performance indicators from the medical arena and "leading health indicators" from the public health arena. The focus on specific conditions by the medical care system has been at odds with public health's emphasis on improving population health and has perpetuated a bifurcated system. Aligning the goals of medical care with those of public health will require reformulation of performance measurement and accountability into a common language that is valued by both systems. Such a creation would amount to a whole that is stronger than the sum of the component parts.

A general model of the impact of absenteeism on employers and employees.

Most studies on the indirect costs of an illness and the cost effectiveness of a medical intervention or employer-sponsored wellness program assume that the value of reducing the number of days employees miss from work due to illness is the wage rate. This paper presents a general model to examine the magnitude and incidence of costs associated with absenteeism under alternative assumptions regarding the size of the firm, the production function, the nature of the firm's product, and the competitiveness of the labor market. We conclude that the cost of lost work time can be substantially higher than the wage when perfect substitutes are not available to replace absent workers and there is team production or a penalty associated with not meeting an output target. In the long run, workers are likely to bear much of the incidence of the costs associated with absenteeism, and therefore be the likely beneficiaries of any reduction in absenteeism.

Stanford presenteeism scale: health status and employee productivity.

Workforce productivity has become a critical factor in the strength and sustainability of a company's overall business performance. Absenteeism affects productivity; however, even when employees are physically present at their jobs, they may experience decreased productivity and below-normal work quality--a concept known as decreased presenteeism. This article describes the creation and testing of a presenteeism scale evaluating the impact of health problems on individual performance and productivity. A total of 175 county health employees completed the 34-item Stanford Presenteeism Scale (SPS-34). Using these results, we identified six key items to describe presenteeism, resulting in the SPS-6. The SPS-6 has excellent psychometric characteristics, supporting the feasibility of its use in measuring health and productivity. Further validation of the SPS-6 on actual presenteeism (work loss data) or health status (health risk assessment or utilization data) is needed.